Archive | IIoT


8:13 pm
August 15, 2017
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Video | Lower Cost Devices Have a Place with IIoT

This video from Schneider Electric discusses the ramifications of Industrial Internet of Things, lower cost devices and how enterprises are changing their view of what can be done on the plant floor or from the field. Three subject matter experts from Schneider Electric discuss the new opportunities for visualizing data on the floor but also how customers are assimilating new and older technology.

>> Related Content | Companies Focus on IIoT Networking

>> Click here for more IIoT related developments



1:43 pm
August 14, 2017
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Companies Focus on IIoT Networking

Hold on to your plant-floor tablets. The wave of change to sign off on large and small IIoT initiatives from maintenance technicians is being felt by corporate management.

As an ex-Kraft Foods plant manager-turned colleague used to say, “It’s going to get bloody.”

The heart of the challenge for the past decade—depending on industries—has been how to rapidly funnel critical plant data to operators, maintenance personnel, and plant management. For some time, industrial networking and interoperable plant-floor platforms prevented data exchanges that provided actionable items for operators and managers.

Advances in industrial networking technologies, however, including encrypted messaging based on the industry operability standard OPC UA (, has moved manufacturers to data solutions that don’t involve ripping out existing infrastructure. Also, ISA-95 has provided a standardized interface process for moving control data from the plant floor, i.e., from manufacturing execution systems (MES) to business enterprises.

According to Charlie Gifford, senior advanced manufacturing consultant and contributing member of the ISA-95 Manufacturing Operations Management Working Group, “As explained at the recent ISA-95/International Electrotechnical Commission (IEC) meeting, BHP Billiton presented the application of ISA-95 as a corporate Industrie 4.0 standard and how they invested $2 million (US) dollars in the upgrade of ISA-95.”

As part of the smart manufacturing initiative within ISA-95, major updates include Parts 2, 4, and 5, along with the new standards for Parts 8 and 9, the smart manufacturing framework. In conjunction with this effort, the ISA-95 Process-Centric Message Working Group created an “event-driven architecture” for IIoT applications that decreases data movement between systems.

“If I send an order to the quality lab using the typical request-response messaging approach, there can be anywhere from four to ten messages sent as part of request-response messages,” said Gifford.  “This new operations event model bundles all changed objects into a single self-describing message, which is published using the new notification model in the Part 5 update.”

In a recent ProFood article (, Gifford noted the Process-Centric Message group is now identifying 20 to 30 common operation events (Level 3) in the new Part 9 of the ISA-95 standard. “Those common 30 Level 3 operation events—dispatching production, scheduling, reliability, etc.—we’re defining the data sets and objects, so when those events occur, I simply publish the whole block of information for the event in one message,” he explained.

The new operations-event model and Part 9 standard also apply to maintenance scheduling, dispatch, and tracking work in progress. For example, a work-completed event would be published by the enterprise asset management (EAM) system and received by enterprise resource planning (ERP), plant scheduling, and MES to inform those applications that the equipment resource is in an available state with a given capability and capacity. These updates could be final by Dec. 2017.

While plant personnel sift through new standards for Industrie 4.0, executives need assistance in approving IIoT pilot projects. “As you’re designing a project or a transformation, you want to design in some early wins, even if they’re simple and small,” said Robert MacNeil, senior technical advisor, Nova Scotia Power (, Halifax) at the 2017 ARC Industry Forum.

‘For The Long Haul’

According to the recently released Price Waterhouse Coopers’ (PwC) “Digital Factories 2020” study, respondents this year don’t seem to be looking for quick fixes from IIoT projects. They’re “in it for the long haul,” expecting to see a return on investments over the next two to five years, wrote Reinhard Geissbauer, head of PwC’s Industry 4.0 in Europe in a recent blog post. Click here for a breakdown by sector.

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at


7:22 pm
August 10, 2017
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Drowning In Data? Look To The ‘Stars’

Identifying and acting on the right data can transform reliability and maintenance programs from resource black holes to key business drivers.

By Jane Alexander, Managing Editor

Advances in common communication protocols and wireless networks have created the Industrial Internet of Things (IIoT), technology that connects everything from material supply through manufacturing to product shipping. As IIoT data quantity increases, plant personnel are in danger of drowning in a flood of information. The dilemma for many is how to make sense of it all and derive answers that help them successfully operate and maintain their processes. Keith Berriman of Emerson (Round Rock, TX) advises to “look to the stars.”

To put things in context, a bit of history is in order, beginning with the ancient Greeks. They, according to many scholars, were some of the first people to recognize patterns among the seemingly endless numbers of stars filling the night skies from horizon to horizon. Assigning names to groups of conspicuous stars, i.e., constellations, they wove references to them into their beliefs, literature, and other forms of cultural expression. Over the centuries, explorers and others have looked to many of these constellations to locate certain stars that could help them navigate the globe.

While not advocating that plant personnel take up actual celestial navigation, Berriman encourages them to consider a similar approach when dealing with the seemingly endless amounts of IIoT-generated data they’re confronting. As he explained, they can locate specific “stars” in their facilities that will tell them about the condition of assets and processes and, in turn, allow them to take action to prevent and mitigate failure. It’s an approach that’s feasible for virtually any plant.

“From an economic perspective,” Berriman said, “the cost of installing connected devices that run on wireless networks has fallen to less than 20% of traditional wired devices. This allows us to install sensors on all sorts of equipment that we previously would have to monitor with hand-held devices or through some type of invasive inspection.” That’s the good news.

The bad news is, despite the affordability and widespread availability of continuous-monitoring technologies, personnel still need to know what to look for amid the data that constantly streams from them. Unfortunately, all systems for analyzing such information are not created equal. “Depending on the system you use,” Berriman observed, “you may not be getting a full and correct picture of equipment and process conditions in your plant.” This is where his “look to the stars” approach to data pays off.

Bringing order to chaos

Berriman’s approach starts with sorting data into fixed and variable groups. “This,” he said, “helps us solve the risk-identification and -mitigation equation.”

Fixed data is set when the plant or system is built or modified. This includes:

• plant layout
• equipment design
• equipment data
• material master data (spare/OEM parts)
• performance parameters
• potential failure data.

These items become the known variables in the risk-identification and -mitigation equation. Variable data, though, changes during the operation of a process or asset, including, among other things, as a result of raw-material composition, process variation, weather, equipment condition, and work history.

By selecting the right data points, personnel can populate the equation and determine their position, which, in this case, means the condition of their site’s assets. Doing this requires building a set of “constellations” to identify and capture critical asset data.

A reliability program is designed to proactively identify and mitigate failures, while eliminating defects. A maintenance program is designed to preserve or restore function to a system. Effective data constellations allow reliability and maintenance teams to detect and repair problems before they have an impact on performance.

Data and reliability programs

An effective reliability program consists of interconnected building blocks that include the following four steps, aimed at identifying impending failures with enough warning to allow repair or replacement. Root Cause Failure Analysis (RCFA) determines the causes of unexpected failures to improve the program and avoid similar events.

Build a complete master equipment list (MEL). The MEL includes the fixed data for the next steps in the process and the information required for planning and scheduling work and ordering parts and materials.

The MEL also contains an organized hierarchy of assets that users can follow to identify equipment. Ideally, the branches should extend down to the “functional location,” i.e., the place in the process where an asset operates. Associating a particular asset with a unique identifier allows it to be tracked as it moves from one location to another.

To complete the MEL, fixed data must be associated with each asset. This includes, among other things:

• equipment type (pump, motor) classification (centrifugal), location, process and operating information, process drawings, size, power, material of fabrication, and motor-frame size

• bills of material (BOMs), i.e., spare parts needed to make repairs to the equipment.

CMMS systems organize and sort this information in various ways and allow the roll-up of metrics, costs, and information to identify performance and trends.

Rank asset criticality. With an accurate MEL, sites can rank the criticality of their assets. While organizations often focus on one potential impact, such as production or safety, to completely understand the relative criticality of their equipment systems, they need to review a number of factors. Five basic categories are used to determine asset criticality:

• safety
• environment
• production
• maintenance cost
• quality.

Additional categories may be used and the weighting adjusted for the specific process under review. Weighting uses a series of questions with points associated with the severity of impact.

Ranking asset criticality requires data and expertise. The resulting distribution can be sorted into categories to determine the next level of analysis and develop preventive- and predictive-maintenance (PM and PdM) programs. Criticality should also be used to prioritize work and ensure high-risk issues are addressed in time to prevent failure.

Develop strategies. At this point, strategies to detect and mitigate impending failures can be developed. Tools for doing so include Reliability Centered Maintenance (RCM) and Failure Modes and Effects Analysis (FMEA). They ask structured questions about the function of an asset, how it might fail, the impact of failure, and how to detect signs of failure. Since RCM requires a team of subject-matter experts and significant time, it should focus on the critical group of assets and systems. FMEA, which can be conducted by one or two participants, should focus on the essential group. Templates can be used to create strategies for the monitor group. In applying templates, it’s crucial to understand the context of an asset, given the fact the same equipment in different locations may not require the same strategy.

Note: Since the impact of their failure isn’t great, assets that fall into a No Scheduled Maintenance group won’t require routine or continuous monitoring.

Select PM/PdM condition-monitoring tools. Understanding failure modes allows personnel to select the appropriate tools for the job. Typically, this selection is based on the warning that a tool provides and the cost of performing the task. The classic P-F (performance-failure) curve illustrates the relative effectiveness of different techniques. IIoT data allows sites to combine indicators and move further back up this curve to provide earlier warnings of failure and, thus, allow plant personnel more time to plan repairs and procure replacements.

Once personnel know the data they require from a site’s network of instruments, analytics, and inspections, they can generate alerts and warnings to restore assets to good operating condition. The more advanced warning they have, the more planned and organized they can be. To that end, they should set warning alarms that allow time to plan and action alarms that indicate when prompt intervention is required. These alarms, and the data they generate, are an important part of the solution to the risk-identification and -mitigation equation, in that they help determine asset condition. As Berriman emphasized, however, “The information must still be acted on.”

Data and maintenance programs

Regardless of industry sector, type of operation, or location, one constant is the basic maintenance process. All plants need to complete the following six steps to be consistent, strong performers

Identify work. Maintenance work is identified through a variety of sources. Most work should come from PM/PdM activities and the previously described warnings and action alerts. However, there will be issues identified by operations that the program missed, requested improvements, and other tasks. These issues need to be reviewed and approved before effort is expended on planning and scheduling.

Work entering the system needs to be reviewed for the completeness of information and approved before moving to planning. Known as gate keeping, this requires a dedicated resource for consistency. Ideally, the gate-keeping role belongs to Operations, i.e., the equipment owners.

Plan work. Planning is where a job is broken down into a logical sequence of tasks, maintenance craft assigned, parts ordered, and other resources identified, including such things as scaffolding and contractors. A good job plan allows accurate scheduling and work execution. Job plans should include safety and environmental precautions, work permits, and other procedures. Data collated at this step should include equipment data, materials/parts data, work history, safety/environmental data, and resource availability.

The output of this step is a backlog of planned work to build schedules and balance workforce composition, especially where contract resources are used to augment in-house maintenance personnel.

Schedule work. This step takes the job plan data for duration and resources, and integrates production-planning data and asset criticality to create a maintenance schedule that fits the production schedule. This requires collaboration between departments to understand priorities, equipment availability, and other issues. The scheduler role should be owned by Operations since, again, it owns (controls) the equipment.

The outputs of scheduling are long-range plans and a weekly calendar of maintenance work used to create daily schedules. Daily scheduling is a joint effort to select new work for the next day from the weekly schedule and to ensure incomplete work is carried forward.

Execute work. When a day’s schedule is completed, Operations can prepare the equipment and Maintenance can execute the work. This phase includes the integration of unplanned work that might supersede scheduled tasks, known as break-in work. This work needs to be managed to prevent organizations from becoming highly reactive.

Maintenance supervisors need to monitor progress on work to communicate with Operations and to ensure time is added to the next day’s schedule for incomplete work.

Follow up/capture data. Upon completion of work, data must be captured to drive analysis, planning, and other activities. That includes capturing “as found/as left” data for instruments, repair history, failed components, time, materials, and labor, among other things. The information should then be recorded in the CMMS for future use. Responsibility for this step typically falls to maintenance technicians and supervisors.

Analyze data. Once data has been captured, analysis can be performed on failure modes to determine and mitigate bad actors, or equipment with high costs and downtime. Cost and lost-production data can be used to understand budget variances and drive key performance indicators (KPIs). Reliability teams use maintenance data for detailed statistical analysis, such as Weibull, that identify patterns of failure and predict future events.

Navigating your data

According to Keith Berriman, the Industrial Internet of Things is an opportunity to increase the generation of accurate timely data without the use of invasive and time-based processes. As the integration of systems improves, the interconnectedness of data allows more accurate and simplified presentation of information for repair/replace decisions.

“But,” he cautioned, “too much unnecessary data can obscure the information personnel are looking for and hide problems that might become critical and dangerous. While technology is a great enabler, without a strong foundation, it won’t deliver the results plants seek. “The key,” Berriman concluded, “is to be able to identify and then act on the right data.” Looking to specific “stars” in your plant is a good way to ease that voyage. MT

Keith Berriman P.Eng, CMRP, is a senior reliability consultant for Emerson, based in Edmonton, Alberta. For more information, email


7:13 pm
August 10, 2017
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Study Helps You Evaluate Your Industry 4.0 Future

parrmugAlmost any conversation you have these days involving manufacturing technology will either begin or end with discussion about Industry 4.0 (Manufacturing 4.0, Internet of Things, Industrial Internet of Things, cloud computing, smart factories). In my analysis, so much of the talk is just talk. People realize that they need to embrace Industry 4.0, in many cases, because others say so, and in some cases because they know that it is the future of manufacturing, that the future is here, and that many are already getting left behind.

The questions on my mind are where companies may be on the continuum that moves them to full implementation of Industry 4.0 technology and what factors must be addressed? I recently received answers in the form of a white paper summarizing research that Frost & Sullivan’s Digital Industrial Group ( conducted in collaboration with NTT Data Services, Plano, TX.

The publication, “Manufacturing 4.0: A Playbook for Navigating the Journey to IT Modernization & Transformation,” outlines six critical issues in today’s manufacturing ecosystems that must be addressed if an enterprise is going to embrace and benefit from Industry 4.0. (The white paper refers to it as Manufacturing 4.0. It would really help if everyone would just settle on one name.) Of those six issues, the research data from four of them paint a rather strong picture about the current status of Industry 4.0 in manufacturing.

New research from Frost & Sullivan and NTT Data Services reveals just where manufacturing enterprises are on the path to full Industry 4.0 implementation.

New research from Frost & Sullivan and NTT Data Services reveals just where manufacturing enterprises are on the path to full Industry 4.0 implementation.

The Factories of the Future issue looks at end-to-end digitalization of manufacturing processes, i.e., IP-enabled factories. The research question was “How extensively has your company IP-enabled and networked its plant-floor equipment today and what do you expect the extent will be in five years’ time? Responses were not a surprise: 32% answered “Partially” for today and 46% said they are just getting started. Only 9% answered “Extensively” for today. The five-years-from-now answer was a mixed bag: 34% answered “Partially” and 55% answered “Extensively.”

The issue of Transformative Technologies asks for the most important business factor driving a company’s move toward Manufacturing 4.0. The leading factor, by a wide margin, was Operational Efficiency at 32%. The next closest factor, at only 17%, was “Increased competition due to globalization,” followed by “Customer expectations” at 16%.

The Next-Generation Manufacturing Leadership issue is also revealing. The question: What do you think are the three most significant challenges to implementing Manufacturing 4.0 in your company? “Understanding the benefits/challenges” led the way at 37%, followed by “Corporate culture” at 29% and “Lack of buy-in from the C-suite” at 26%. Tied at 25% were “Finding skilled people,” “Change management,” “Developing a Manufacturing 4.0 strategy,” and “Identifying opportunities and ROI.”

That Next-Generation Manufacturing Leadership issue tells me what I’ve suspected: Most people are at the beginner end of the spectrum and need substantial education and help. But there’s also a question of how convinced enterprises are that Industry 4.0 really is the future. In the Changing Workforce Dynamics issue, 42% say that Manufacturing 4.0 is “A game changer, truly a new era.” But 50% feel that it’s “Significant, but not transformative.”

I’ve only hit the highlights of this research report. No matter where you are on the path to full Industry 4.0 implementation, this study is compelling. Download it here. MT


2:59 pm
August 9, 2017
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GE Adds More IIoT Services to their Portfolio

xgeoogoThe devil is always in the details, but a new service model from GE, called Acceleration Plans, offers to help customers grow with IIoT applications, be it with maintenance, training, or the adoption of managed service capabilities.

According to the press release from late July, Acceleration Plans offer three tiers of increasing value depending on customer needs. These tiers provide the necessary tools and guidance to make new software installation and adoption successful and help maximize return on investment.

“Only 15% of software deployments are considered very successful. Organizational readiness, support, training and data quality need to be accounted for if positive business outcomes are going to be generated,” says Chad Naeger, Senior Vice President and Chief Customer Success Officer for GE Digital.

Maintenance Technology’s Take
Manufacturers struggle with mountains of plant data is well documented, along with new platforms and processes is begging for service experts to provide an essential role for most manufacturers. More services is where GE is going — along with others. GE also offers machine health kits called Digital Machine & Equipment health starter kits to help manufacturers start small and evaluate machine monitoring strategies. For more information on these kits, visit Rapid Start Services page.

>> More details about Acceleration Plans can be found here.


7:45 pm
July 26, 2017
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Interview | Insights on How to Engage with IIoT

The headline could also be another way of saying, how to sell IIoT to management? A recent webinar via Maintenance Technology (via link) revealed how a Hank Ward, aGlobal Asset Management Lead at MolsonCoors approached selling a MRO data optimization project to management. In Ward’s case, he tied it into a bigger Enterprise Resource Planning (ERP)integration project to justify the cost.

In this seven-minute video, it’s more of the same. Brun Mommens, IoT and M2M Solutions Lead for Delaware Consulting, talks about how to engage the IIoT process within a company and, of course, sell it to decision makers. Topics include organization design thinking sessions, IT/OT, and case studies.


7:30 pm
July 20, 2017
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White Paper | Digitizing Vibration-Based Condition Monitoring

1707wp_proftechnikThis white paper provides a workforce perspective on automating the process of acquiring vibration data on rotating equipment, along with engineering practices and a case study on condition monitoring with wind turbines — fastest growing profession in the U.S. is a wind turbine technician.

The white paper comes from German-based PRUFTECHNIK Inc. and here’s an excerpt of the white paper:

In this white paper we are exploring how these new technologies will empower technicians and engineers to efficiently and accurately predict and analyze wear and damages in rotating equipment and how these new technologies are boosting the effectiveness of the vibration analyst. The result is an accrued efficiency of industrial production units, marine vessels and offshore units, rendering them safer, less polluting and more profitable.

If the skill of vibration analysts could be used mainly to analyze problems rather than going through huge amounts of data or walking through the factory to collect data, then this would decuple the efficiency of the analyst and – along the way – remove the often boring part of the job. Automating the data acquisition, generating exception reports, recognizing aberrant conditions and even identifying or eliminating plausible causes for an aberrant vibration signal will certainly point the analyst in the right direction.

Download the White Paper Here >>


6:57 pm
July 12, 2017
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Adhere to a ‘Best Practices’ Cyber Framework

Security concept: Lock on digital screen, contrast, 3d renderIn 2013, the United States’ National Institute of Standards and Technology (NIST, Gaithersburg, MD, was tasked with developing a framework that would become an authoritative source for cybersecurity best practices. Other countries have similar standards or are actively working on versions. In some places, such as France, these standards carry the weight of law.

According to Andrew Kling, director of Cybersecurity and Software Practices for Schneider Electric (, Andover, MA), the standards that emerged from the NIST framework established an ordered, structured approach to addressing cybersecurity challenges and helped translate vague, fear-based concerns into commonsense risk analysis, risk-tolerance assessment, and risk avoidance.

“Confronting the cybersecurity challenge as part of a focused risk-management program,” Kling noted, “allows an organization to take on one of the biggest threats to its ability to deliver shareholder value. For plants to operate profitably, they must protect the reliability of their assets and operations. Cybersecurity attacks threaten their reliability, which in turn jeopardizes their ability to turn a profit.”

randmHe explained that, while the set of core cybersecurity practices necessary to manage cyberthreats are well known, there are still barriers to adoption. For the most part, these obstacles are related to an improper understanding of the risks at hand, as well as to an organization’s ability to resist them.

Consequently, despite regulatory and risk-management incentives, Kling said finding companies that effectively address cybersecurity is rare. To his way of thinking, it’s time to change the conversation away from the fear of a cyber attack to something understood in all boardrooms: How do cyber attacks threaten the reliability of plant assets and operations and their ability to contribute to the bottom line.

This requires managers to know and understand their plants’ cybersecurity positions and appetites for risk tolerance. This information helps them recognize the difference between where they are managing cyber risks and how much gap there is to close. Here’s where a strategy to improve an operation’s cybersecurity readiness through comprehensive security-risk management pays off.

Crucial steps

What’s an operation to do? Andrew Kling points to these specifics:

• Discuss and understand your risk-management plan and objectives (which usually means protecting your ability to produce).

• Locate responsibility for risk management in your organization so that decision making, execution, and incident response are efficient and successful. Assess your risk-management workflows.

• Ascertain the value of your manufacturing processes and assets to your organization and potential attackers. Basically, you need to calculate your security risk. For example: If the plant were to go down for a day due to a cyber attack, loss of production would equal $X.

• Model the cyber-threat landscape. Analyze threats specific to your industry and your plant. Remember that threats are constantly evolving as new skills, techniques, and tools emerge. You might need expert help.

• Determine where security-risk-management functions should integrate into your organization’s infrastructure. These functions can take many forms, i.e., risk avoidance, mitigation, acceptance, and/or transference.

• Construct a cybersecurity plan that lets the organization respond to an evolving threat landscape. Analyze options to the plan and rank the effectiveness of its elements in reducing risks.

• Prioritize and execute the plan to manage your organization’s cyber risks.

• Keep in mind that program elements, such as bug patching and threat monitoring, are continuous. A cybersecurity risk-management plan isn’t a single event, but a continuous operation.

In short, have a plan, execute it, measure its effectiveness, and, if necessary, adjust it. Taking these simple steps to manage your cybersecurity risks can have a significant impact (in a good way) on your bottom line. MT

—Jane Alexander, Managing Editor

For more information, visit and